A review of the threat of oil exploitation to mangrove ecosystem: Insights from Niger Delta, Nigeria

Onyena, Amarachi Paschaline; Sam, Kabari

doi:10.1016/j.gecco.2020.e00961

Cited by 87 publications

(66 citation statements)

References 41 publications

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“…Accurate and high-resolution mangrove forest extent data products at large spatial scales are needed for mangrove change analysis (Lagomasino et al, 2019) and regular monitoring (Emuedo et al, 2014;Kuenzer et al, 2014;Duke 2016;Onyena and Sam, 2020). The 2017 mangrove extent maps provided here are the highest quality and highest resolution data products for West Africa region so far, which can be used by the wider research community to derive more accurate estimates of other mangrove properties, including mangrove aboveground biomass (AGB) and carbon (Hutchison et al, 2014;Simard et al, 2019) and belowground carbon (Rovai et al, 2018;Sanderman et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

Large-Scale High-Resolution Coastal Mangrove Forests Mapping Across West Africa With Machine Learning Ensemble and Satellite Big Data

Xue

Fatoyinbo²,

Thomas

et al. 2021

Front. Earth Sci.

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Coastal mangrove forests provide important ecosystem goods and services, including carbon sequestration, biodiversity conservation, and hazard mitigation. However, they are being destroyed at an alarming rate by human activities. To characterize mangrove forest changes, evaluate their impacts, and support relevant protection and restoration decision making, accurate and up-to-date mangrove extent mapping at large spatial scales is essential. Available large-scale mangrove extent data products use a single machine learning method commonly with 30 m Landsat imagery, and significant inconsistencies remain among these data products. With huge amounts of satellite data involved and the heterogeneity of land surface characteristics across large geographic areas, finding the most suitable method for large-scale high-resolution mangrove mapping is a challenge. The objective of this study is to evaluate the performance of a machine learning ensemble for mangrove forest mapping at 20 m spatial resolution across West Africa using Sentinel-2 (optical) and Sentinel-1 (radar) imagery. The machine learning ensemble integrates three commonly used machine learning methods in land cover and land use mapping, including Random Forest (RF), Gradient Boosting Machine (GBM), and Neural Network (NN). The cloud-based big geospatial data processing platform Google Earth Engine (GEE) was used for pre-processing Sentinel-2 and Sentinel-1 data. Extensive validation has demonstrated that the machine learning ensemble can generate mangrove extent maps at high accuracies for all study regions in West Africa (92%–99% Producer’s Accuracy, 98%–100% User’s Accuracy, 95%–99% Overall Accuracy). This is the first-time that mangrove extent has been mapped at a 20 m spatial resolution across West Africa. The machine learning ensemble has the potential to be applied to other regions of the world and is therefore capable of producing high-resolution mangrove extent maps at global scales periodically.

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Section: Discussionmentioning

confidence: 99%

Large-Scale High-Resolution Coastal Mangrove Forests Mapping Across West Africa With Machine Learning Ensemble and Satellite Big Data

Xue

Fatoyinbo²,

Thomas

et al. 2021

Front. Earth Sci.

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“…For instance, in a bid to ushering in a new year in the Niger Delta area of Nigeria, community members undergo festive bathing as it is believed to be integral to the prosperity of the community in the new year. Cultural beliefs like this, among many others, have been obliterated due to oil spills emanating from crude oil exploration (Onuegbulam 2018 ; Onyena and Sam 2020 ). Other economically viable aquatic species such as Eastern oyster ( Crassostrea virginica ), Nile tilapia ( Oreochromis niloticus ), rainbow trout ( Oncorhynchus mykiss ) are negatively impacted by oil pollution (De Anna et al 2021 ; Garcia et al 2020 ; Olajuyigbe et al 2020 ).…”

Section: Socio-economic Impacts Of Crude Oil Exploration and Processingmentioning

confidence: 99%

Crude oil exploration in Africa: socio-economic implications, environmental impacts, and mitigation strategies

et al. 2021

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Crude oil exploration is a source of significant revenue in Africa via trade and investment since its discovery in the mid-19th Century. Crude oil has bolstered the continent’s economy and improved the wellbeing of the citizenry. Historically, Africa has suffered from conflicts due to uneven redistribution of crude oil revenue and severe environmental pollution. Advancements in geophysical survey techniques, such as magnetic and gravity methods, to seismic methods, have made the commercial exploration of crude oil possible for some other countries in Africa apart from Nigeria, Angola, Algeria, Libya, and Egypt. The occurrence of organic-rich, oil-prone Type I, II, and mixed II/III kerogens in sedimentary basins and entrapment within reservoir rocks with intrinsic petrophysical properties are majorly responsible for the large deposits of hydrocarbon in Africa. The unethical practices by some multinational oil corporations have resulted in social movements against them by host communities and human rights groups. The unscrupulous diversion of public funds, award of oil blocks, and production rights to certain individuals have impaired economic growth in Africa. The over-dependence on crude oil revenues has caused the economic recession in oil-producing countries due to plummeting oil prices and global pandemic. Most host communities of crude oil deposits suffer from a lack of infrastructure, arable soils, clean water, and their functioning capabilities are violated by crude oil exploratory activities, without adequate compensations and remedial actions taken by oil companies and the government. Thus, this review examines crude oil exploration in Africa and provides insight into the environmental and socio-economic implications of crude oil exploration in Africa. Furthermore, this report highlights some recommendations that may ensure ethical and sustainable practices toward minimizing negative impacts and improving the quality of life in affected communities.

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“…These cover the study area with data from November 2009 to January 2017. About 90% of the study area is covered with 46-cm-pixel data from GeoEye-1 (10 December 2010, 16 . Thanks to the familiarity with the study area, the broad land cover classes that were targeted in this paper were relatively easily identifiable on the very high-resolution imagery.…”

Section: Reference Datamentioning

confidence: 99%

Land Cover Dynamics and Mangrove Degradation in the Niger Delta Region

et al. 2020

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The Niger Delta Region is the largest river delta in Africa and features the fifth largest mangrove forest on Earth. It provides numerous ecosystem services to the local populations and holds a wealth of biodiversity. However, due to the oil and gas reserves and the explosion of human population it is under threat from overexploitation and degradation. There is a pressing need for an accurate assessment of the land cover dynamics in the region. The limited previous efforts have produced controversial results, as the area of western Africa is notorious for the gaps in the Landsat archive and the lack of cloud-free data. Even fewer studies have attempted to map the extent of the degraded mangrove forest system, reporting low accuracies. Here, we map the eight main land cover classes over the NDR using spectral-temporal metrics from all available Landsat data centred around three epochs. We also test the performance of the classification when L-band radar data are added to the Landsat-based metrics. To further our understanding of the land cover change dynamics, we carry out two additional assessments: a change intensity analysis for the entire NDR and, focusing specifically on the mangrove forest, we analyse the fragmentation of both the healthy and the degraded mangrove land cover classes. We achieve high overall classification accuracies in all epochs (~79% for 1988, and 82% for 2000 and 2013) and are able to map the degraded mangroves accurately, for the first time, with user’s accuracies between 77% and 87% and producer’s accuracies consistently above 82%. Our results show that mangrove forests, lowland rainforests, and freshwater forests are reporting net and highly intense losses (mangrove net loss: ~500 km2; woodland net loss: ~1400 km2), while built-up areas have almost doubled in size (from 1990 km2 in 1988 to 3730 km2 in 2013). The mangrove forests are also consistently more fragmented, with the opposite effect being observed for the degraded mangroves in more recent years. Our study provides a valuable assessment of land cover dynamics in the NDR and the first ever accurate estimates of the extent of the degraded mangrove forest and its fragmentation.

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A review of the threat of oil exploitation to mangrove ecosystem: Insights from Niger Delta, Nigeria

Cited by 87 publications

References 41 publications

Large-Scale High-Resolution Coastal Mangrove Forests Mapping Across West Africa With Machine Learning Ensemble and Satellite Big Data

Large-Scale High-Resolution Coastal Mangrove Forests Mapping Across West Africa With Machine Learning Ensemble and Satellite Big Data

Crude oil exploration in Africa: socio-economic implications, environmental impacts, and mitigation strategies

Land Cover Dynamics and Mangrove Degradation in the Niger Delta Region

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